---
category: 17-upscaling-refinement
angle: 17d
title: Logo / Icon Specific Refinement — Sharp Edges, Crisp Geometry, Snap-to-Grid
slug: logo-icon-sharpness-refinement
author: research-subagent-17d
date: 2026-04-19
tags:
  - upscaling
  - logos
  - icons
  - flat-art
  - vectorization
  - edge-refinement
  - snap-to-grid
  - DAT-2
  - SPAN
  - Nomos2
  - potrace
  - vtracer
status: draft
---

# 17d — Logo / Icon Specific Refinement

## Executive Summary

Photographic super-resolution models (Real-ESRGAN x4plus, SUPIR, SwinIR-L) are actively *hostile* to logo and icon assets. They were trained to hallucinate grain, film noise, pore texture, and natural-image micro-detail — exactly the things a logo must not have. Run a brand mark through SUPIR and you get a "plastic and overly smooth" rasterized illustration with invented micro-gradients, anti-aliased halos around every edge, and pixel-misaligned geometry. The result is visually impressive at 4K and catastrophically wrong as a brand asset.

For flat art — logos, app icons, favicons, UI glyphs, stickers — the refinement pipeline must be built around three principles:

1. **Choose a flat-art-tuned upscaler** (SPAN, DAT-2 illustration variants, Nomos2 HQ, Real-ESRGAN Anime 6B) or skip learned upscaling entirely (nearest-neighbor, hqx, xBR for true pixel art).
2. **Prefer vector round-trips** over raster upscaling when the asset is genuinely flat: generate → matte → quantize colors → vectorize (potrace / vtracer) → rasterize at the target size. This is lossless and produces mathematically perfect edges at any scale.
3. **Snap back to the pixel grid** with Sobel/Otsu edge recovery, morphological cleanup, and integer-pixel alignment before exporting final artifacts. "Pixel-perfect" is not a style; it is a rendering requirement for iOS/Android/Material/Spectrum/Carbon icons [1].

The single biggest win in the prompt-to-asset system will be **detecting intent** ("logo", "icon", "favicon", "flat illustration") and routing to a **vectorize-then-rasterize** branch instead of the default diffusion-upscale branch used for photos and hero renders. Straight upscaling should be reserved for stylized or complex illustrations where vector tracing would destroy intended texture.

---

## Logo-Safe Upscaler List

The following models are specifically trained, fine-tuned, or architecturally suited to flat art (logos, icons, line work, anime-style cel shading). All are available on [OpenModelDB](https://openmodeldb.info/) in `.pth`, `.safetensors`, and ONNX formats. Sizes and iteration counts are taken from the OpenModelDB model cards.

### Tier 1 — Flat-art native

- **4x IllustrationJaNai_V1_DAT2** — DAT-2 backbone explicitly tuned for manga, color illustrations, visual novel art, and artbooks. The DAT-2 variant is the highest quality (and slowest) of the IllustrationJaNai line. This is the first model to try on stylized flat illustrations and polished logos. Handles broad flat color fields without over-sharpening. [OpenModelDB card](https://openmodeldb.info/models/4x-IllustrationJaNai-V1-DAT2).
- **4x UltraSharpV2 (DAT-2)** — 2025 DAT-2 model trained on a mixed private dataset. Strong generalist for illustrations, cartoons, and anime, with explicit reports of handling flat color regions cleanly. Good fallback when IllustrationJaNai over-smooths. [OpenModelDB card](https://openmodeldb.info/models/4x-UltraSharpV2).
- **Real-ESRGAN x4plus Anime 6B** — the original anime-specific Real-ESRGAN variant. 6 RRDB blocks vs. 23 in the photo model, ~17 MB, very fast. Preserves flat cel-shaded color fields and hard line art, where `RealESRGAN_x4plus` blurs them. Still the workhorse for anime-style and flat-illustration logos. [Docs](https://www.mintlify.com/xinntao/Real-ESRGAN/guides/anime-images).

### Tier 2 — Clean-input generalists (great for AI-generated flat art)

- **4xNomos2_hq_dat2** — DAT-2 trained on the 6,000-image Nomos-v2 dataset with *non-degraded* input. Because AI-generated flat art is typically clean (no JPEG artifacts, no camera noise), the HQ variant outperforms the degradation-trained variants. [OpenModelDB card](https://openmodeldb.info/models/4x-Nomos2-hq-dat2).
- **4xNomos2_hq_mosr** — MoSR architecture on the same dataset, 16 MB, faster than DAT-2 with similar fidelity on clean flat inputs. [OpenModelDB card](https://openmodeldb.info/models/4x-Nomos2-hq-mosr).
- **4xNomos2_realplksr_dysample** — RealPLKSR + Dysample, trained *with* JPEG quality-70 degradation. Use this one only when the input has been round-tripped through lossy compression (e.g., a customer-supplied logo from a PNG screenshot). [OpenModelDB card](https://openmodeldb.info/models/4x-Nomos2-realplksr-dysample).
- **4x SPANkendata** — 1.6 MB ONNX, SPAN architecture. Built as a *pretrain* model for clean photos but works surprisingly well on clean, noise-free AI outputs. The small size makes it ideal for in-browser / edge / Lambda pipelines. [OpenModelDB card](https://openmodeldb.info/models/4x-SPANkendata).
- **4x ClearRealityV1** — SPAN, ~9 MB, tuned for softer/natural output. The "soft" variant is useful as an *intermediate* pass before harsher edge refinement, not a final pass for logos. [OpenModelDB card](https://openmodeldb.info/models/4x-ClearRealityV1).

### Tier 0 — No learned upscaling

For true pixel art, retro icons, or any asset where the *intent* is visible pixels:

- **Nearest-neighbor** at integer scale factors (2×, 4×, 8×, 16×). Preserves exact edges, introduces zero halos, zero hallucination. The canonical correct answer for pixel art. [Reference](https://image-scaler.com/blog/nearest-neighbor-interpolation/).
- **hqx / xBR / xBRZ** — pattern-based pixel-art upscalers that smooth diagonals while preserving the pixel aesthetic. Use when nearest-neighbor is too jagged on 45° lines but learned upscalers would destroy the style. [hqx explainer](https://every-algorithm.github.io/2024/10/30/hqx.html).

### Models to avoid for logos

| Model | Why it fails on logos |
|---|---|
| Real-ESRGAN x4plus (photo) | Hallucinates skin/pore/grain texture into flat fills |
| SUPIR | Photo-realistic generative prior adds plastic gloss and invented gradients; removes crisp edges ([SUPIR model card notes outputs look "plastic and overly smooth"](https://replicate.com/astramlco/supir/readme)) |
| SwinIR-L real_sr | Trained on ImageNet-style photos; blurs sharp color boundaries |
| Latent upscaler (SDXL) at high denoise | Re-imagines the logo; color drift, shape drift, text corruption |

> **Rule of thumb:** if the model card says "photo restoration", "in the wild", "real-world degradation", "film grain", or "faces/portraits", it will fight you on a logo.

---

## Edge-Snapping Recipe

After a learned upscale, AI-generated flat art typically has:

- **Soft halos** (1–3 px of anti-aliased gradient around hard edges)
- **Sub-pixel drift** (edges that land on 127.4 / 128.6 instead of a clean 128)
- **Gradient noise** in "solid" background fills (the artifact that also produces Gemini's infamous transparency checkerboard)

The following deterministic post-process cleans all three without re-running diffusion.

### Step 1 — Flatten the background

If an alpha matte is available (from a prior rembg / BiRefNet / RMBG-2.0 pass — see 16b), zero out every RGB pixel where `alpha < ε`. This removes the "ghost" halo in the RGB channels that survives alpha thresholding. If no matte exists, quantize the background to a single pure color first (see Step 2).

### Step 2 — Color quantization (posterize)

Reduce the palette to the expected number of logo colors (typically 2–12). K-means in RGB or Lab space gives the cleanest clusters.

```python
from sklearn.cluster import KMeans
import numpy as np
from PIL import Image

img = np.array(Image.open("upscaled.png").convert("RGB"))
h, w, _ = img.shape
flat = img.reshape(-1, 3)

sample = flat[np.random.choice(len(flat), 5000, replace=False)]
km = KMeans(n_clusters=6, n_init=10, random_state=0).fit(sample)
labels = km.predict(flat)
quantized = km.cluster_centers_[labels].astype(np.uint8).reshape(h, w, 3)

Image.fromarray(quantized).save("posterized.png")
```

The [scikit-learn example](https://scikit-learn.org/0.24/auto_examples/cluster/plot_color_quantization.html) and [Wand's `img.kmeans(number_colors=N)`](https://docs.wand-py.org/en/0.6.10/guide/quantize.html) both wrap this cleanly. ImageMagick's built-in `-colors N` and `+dither -posterize` work from the command line for CI use. K-means with ~5,000 pixel samples converges in <100 ms on a 4K logo.

### Step 3 — Sobel edge recovery

Use Sobel gradients + Otsu threshold to isolate logo contours, then morphologically dilate once to regrow a hard edge band:

```python
import cv2, numpy as np
gray = cv2.cvtColor(quantized, cv2.COLOR_RGB2GRAY)
gray = cv2.GaussianBlur(gray, (3, 3), 0)
gx = cv2.Sobel(gray, cv2.CV_32F, 1, 0, ksize=3)
gy = cv2.Sobel(gray, cv2.CV_32F, 0, 1, ksize=3)
mag = cv2.convertScaleAbs(np.hypot(gx, gy))
_, edges = cv2.threshold(mag, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
edges = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8))
```

This gives a binary edge mask with no sub-pixel blur. Otsu's method auto-selects the threshold on the Sobel magnitude distribution, which is more robust than a hand-tuned value across logos of varying contrast. See the [OpenCV Sobel guide](https://thelinuxcode.com/python-program-to-detect-image-edges-with-opencv-sobel-edge-detection-2026-practical-guide/).

### Step 4 — Snap to integer pixels

Once the palette and edges are clean, resize *only* to integer multiples of the source resolution (1024 → 2048, 2048 → 4096) using `cv2.INTER_NEAREST` if the art is already vectorized-looking, or `cv2.INTER_AREA` for downscale. Avoid `INTER_CUBIC` / `INTER_LANCZOS4` when the output target is a small icon — they re-introduce halos.

For design-tool alignment (Illustrator, Figma), match the grid to 1 px with subdivisions = 1 and enable **Snap to Grid** rather than **Snap to Pixel**, as documented by [Bjango's Illustrator snapping guide](https://bjango.com/articles/illustratorsnapping) and the [Icons8 pixel-perfect icon guide](https://blog.icons8.com/articles/make-pixel-perfect-icons). Apple, Google Material, Adobe Spectrum, IBM Carbon, and Firefox Photon all mandate whole-pixel alignment for production icons [1].

### Step 5 — Export with correct resampling

- For **app icons** → export at 1024×1024 and render Apple's [Icon Composer](https://developer.apple.com/documentation/Xcode/creating-your-app-icon-using-icon-composer) masks downstream.
- For **favicons** → export at 16, 32, 48, 180, 192, 512 using `INTER_AREA` or, better, re-rasterize from SVG (see next section) so each size is independently anti-aliased.
- For **hero renders** → see "Hybrid Strategies" below; straight upscale is the wrong choice.

---

## Vectorize-Then-Rasterize Pipeline

For any asset that is fundamentally flat (logos, wordmarks, UI icons, stickers, monograms, emblems), **raster upscaling is the wrong abstraction.** The correct pipeline is:

> **Generate → Matte → Quantize → Vectorize → Re-rasterize at target**

Once you have SVG, there is no such thing as "upscaling" — paths are mathematically sharp at 16 px and at 16,000 px.

### Stage 1 — Generate at a vector-friendly resolution

Two routes:

- **Native vector generation**: Use [Recraft V4 SVG](https://replicate.com/recraft-ai/recraft-v4-svg) (up to 10,000-char prompts, structured layers, production-ready output that needs "no cleanup or path optimization"), Ideogram V3 Quality (when text rendering is the primary requirement), or an SVG-fine-tuned SDXL such as [SVGStud.io](https://jmetzen.github.io/blog/2024/svgstudio-lora/). These skip the tracing step entirely.
- **Raster-then-trace**: Generate at 1024×1024 with Flux.1 Dev or SDXL, ideally with a vector-style LoRA (e.g., `Simple_Vector_Flux_v2`, `v3ctora`). Use a high-contrast prompt, CFG 3.5 for Flux-Distilled or 1 for standard Flux, 25–30 steps, Euler sampler. Boost image contrast 1.5–2× before tracing.

### Stage 2 — Matte to a clean alpha

Run [BiRefNet_HR](https://model.aibase.com/models/details/1915687284843954177) or [briaai/RMBG-2.0](https://huggingface.co/briaai/RMBG-2.0) to produce a high-resolution alpha matte. BiRefNet supports 2048×2048 natively and achieves hair-level matting precision — for a logo, this translates to *sub-pixel* edge accuracy on curves and serifs. Composite the foreground onto pure white or pure transparent, then threshold any alpha < 8/255 to zero to kill the halo.

### Stage 3 — Quantize the color palette

Apply k-means with `k = expected_colors` (most logos: 2–6) as shown in the Edge-Snapping Recipe. This collapses gradient noise in solid fills and makes tracing deterministic.

### Stage 4 — Vectorize

Choose the tracer by input style:

| Input | Tool | Notes |
|---|---|---|
| Black-and-white silhouettes, wordmarks | [**potrace**](http://potrace.sourceforge.net/) | O(n²) curve fitter, binary input only, produces extremely clean Béziers. The gold standard for mono logos. |
| 2–12 color flat logos, stickers, mascots | [**vtracer**](https://github.com/visioncortex/vtracer) | Rust, O(n), hierarchical clustering + path walking + penalty-based simplification + curve fitting. Handles color natively, faster than Illustrator Image Trace, more compact SVG output. |
| Gradient-heavy illustrations | autotrace, Illustrator Image Trace | Vector fidelity drops; consider keeping raster instead. |

Typical vtracer CLI for a 6-color logo:

```bash
vtracer \
  --input logo_1024.png \
  --output logo.svg \
  --colormode color \
  --hierarchical cutout \
  --filter_speckle 4 \
  --color_precision 6 \
  --corner_threshold 60 \
  --segment_length 4 \
  --splice_threshold 45
```

Key knobs: `filter_speckle` drops islands smaller than N pixels (kills residual noise from the diffusion step), `corner_threshold` controls Bézier-vs-line detection, `color_precision` bits per channel. See [VTracer docs](https://www.visioncortex.org/vtracer-docs).

### Stage 5 — Re-rasterize at each output size

Use `resvg`, `librsvg`, or `Inkscape --export-png --export-width=N` to rasterize the SVG at each required size — 16, 32, 48, 64, 128, 180, 192, 256, 512, 1024. Each rasterization independently computes anti-aliasing for that size, which is *the* technical requirement for favicons and app icons to look crisp at small sizes. The raster-upscale path cannot produce this.

### Why this beats straight upscale

- **Lossless at any target size** — one SVG renders for every icon slot.
- **File size** — a 4-color logo SVG is 2–8 KB; a 4096×4096 PNG is 800 KB–3 MB.
- **Editability** — downstream color swaps, stroke-weight tweaks, and brand variants are path edits, not new diffusion runs.
- **Snap-to-grid is free** — round path coordinates to integer pixels before rasterizing and every edge lands on the grid by construction.

The [H3sync write-up on vectorizing AI logos](https://h3sync.com/blog/how-to-vectorise-ai-generated-logo/) and the [VecSmith weekend build](https://llmkube.com/blog/vecsmith-weekend-text-to-svg) both independently converge on this same pipeline (generate → quantize → trace → rasterize).

---

## Hybrid Strategies

Real assets rarely fit a pure "vector" or "raster" bucket. The following hybrids are worth building into the prompt-to-asset routing layer.

### Hybrid A — Generate → Matte → Vectorize → Rasterize at 4×

The canonical hybrid. Generate at 1024, matte with BiRefNet, vectorize with vtracer, rasterize back to 4096 for hero placement and to {16,32,48,64,128,180,192,256,512,1024} for icon slots. In head-to-head tests by [the LLMKube VecSmith team](https://llmkube.com/blog/vecsmith-weekend-text-to-svg) this pipeline produces cleaner 4K renders than Real-ESRGAN x4 for logo-style inputs — because the 4K step has zero learned hallucination.

Use when: the asset is clearly flat (logos, wordmarks, flat illustrations, UI icons).

### Hybrid B — Vectorize only the silhouette, keep the interior raster

For semi-flat illustrations (flat character with soft shading, flat landscape with subtle gradients), a pure vector trace loses the gradients. Instead:

1. Matte the foreground with BiRefNet.
2. Vectorize *only* the silhouette as a clipping path.
3. Upscale the interior raster with DAT-2 (IllustrationJaNai or UltraSharpV2).
4. Composite the upscaled raster inside the vector silhouette at target resolution.

Edges are mathematically perfect (from the SVG clip); interior texture is preserved. This is how Recraft and several vector-native tools handle semi-flat output internally.

### Hybrid C — Tiled SDXL/Flux upscale for coherent 4K *hero* renders

When the deliverable is a hero / marketing image (not a logo) that must be photoreal or painterly at 4K, use [Ultimate SD Upscale](https://github.com/ssitu/ComfyUI_UltimateSDUpscale) with:

- `upscale_by`: 2–4
- `denoise`: 0.2–0.35 (low enough that the logo shape inside the hero doesn't drift)
- `cfg`: 5–7
- `tile_size`: 1024 with Chess or Linear stitch mode
- Upscaler model: `4xNomos2_hq_dat2` as the pre-pass, SDXL for the diffusion pass

This gives coherent 4K content for landing-page hero images. Do **not** use it on bare logos; even at 0.2 denoise the model redraws text and small shapes.

### Hybrid D — LoRA-guided tiled upscale for brand consistency

When upscaling a set of brand assets that must share style (illustrations for an empty-state suite, a sticker pack, an icon family), load a style LoRA or IP-Adapter style-reference during the tiled upscale pass. This keeps line weight, palette, and shading consistent across the set. Pair with a low-denoise tiled SDXL/Flux upscale (Hybrid C) or with a vectorize-then-rasterize flow (Hybrid A) where the LoRA shaped the generation and vectorization preserves it. Covered in more depth in research angles 15b (style consistency) and 17a/17c (Real-ESRGAN/SUPIR/tiled-upscale); mentioned here only because it composes with the logo path when the "logo" is actually one member of a coordinated illustration set.

### Hybrid E — Text-in-logo via Ideogram, shapes via Recraft

Logos with readable wordmarks are a known weakness of Flux/SDXL. A productionable route is:

1. Use Ideogram V3 Quality (via Recraft Studio) for the wordmark alone — it has the best text rendering fidelity of any 2025 raster model [Recraft docs](https://www.recraft.ai/ai-models/ideogram-v3-quality).
2. Use Recraft V4 SVG for the icon / graphic mark.
3. Composite the two in SVG, snap to grid, rasterize per target.

This mixes best-in-class text and best-in-class vector shape generation, which no single model currently does well.

---

## Routing Heuristics for the Prompt Enhancer

The prompt-to-asset skill should detect the asset type from the user's request and pick a branch:

| Signal in request | Branch |
|---|---|
| "logo", "wordmark", "brand mark", "monogram", "emblem" | Hybrid A (generate → matte → vectorize → rasterize) |
| "app icon", "ios icon", "android icon", "launcher icon" | Hybrid A, then Apple Icon Composer / Android adaptive icon wrap |
| "favicon", "pwa icon", "apple touch icon" | Hybrid A, rasterize at full size table |
| "pixel art", "retro", "8-bit", "16-bit" | Nearest-neighbor or hqx/xBR, no learned upscale |
| "flat illustration", "sticker", "emoji style" | Hybrid A or Hybrid B |
| "hero image", "banner", "marketing", "photo-real" | Hybrid C (tiled SDXL upscale) |
| "painterly", "concept art", "fantasy scene" | SUPIR or Hybrid C with high-quality DAT-2 pre-pass |
| (ambiguous) | Generate preview + ask user before choosing raster vs vector branch |

This routing is the single highest-leverage piece of the upscaling system: picking the wrong branch (SUPIR on a logo, nearest-neighbor on a photo) is irrecoverable.

---

## References

1. Helena Zhang. *Pixel-snapping in icon design.* The Atlantic Product Blog / UX Collective. [building.theatlantic.com/pixel-snapping-in-icon-design](https://building.theatlantic.com/pixel-snapping-in-icon-design-a-rendering-test-6ecd5b516522)
2. Icons8. *How to make pixel-perfect icons.* [blog.icons8.com/articles/make-pixel-perfect-icons](https://blog.icons8.com/articles/make-pixel-perfect-icons)
3. Bjango. *My Illustrator snapping settings.* [bjango.com/articles/illustratorsnapping](https://bjango.com/articles/illustratorsnapping)
4. Apple Developer. *Creating your app icon using Icon Composer.* [developer.apple.com/documentation/Xcode/creating-your-app-icon-using-icon-composer](https://developer.apple.com/documentation/Xcode/creating-your-app-icon-using-icon-composer)
5. OpenModelDB. *4x IllustrationJaNai_V1_DAT2.* [openmodeldb.info/models/4x-IllustrationJaNai-V1-DAT2](https://openmodeldb.info/models/4x-IllustrationJaNai-V1-DAT2)
6. OpenModelDB. *4x UltraSharpV2.* [openmodeldb.info/models/4x-UltraSharpV2](https://openmodeldb.info/models/4x-UltraSharpV2)
7. OpenModelDB. *4xNomos2_hq_dat2.* [openmodeldb.info/models/4x-Nomos2-hq-dat2](https://openmodeldb.info/models/4x-Nomos2-hq-dat2)
8. OpenModelDB. *4xNomos2_hq_mosr.* [openmodeldb.info/models/4x-Nomos2-hq-mosr](https://openmodeldb.info/models/4x-Nomos2-hq-mosr)
9. OpenModelDB. *4xNomos2_realplksr_dysample.* [openmodeldb.info/models/4x-Nomos2-realplksr-dysample](https://openmodeldb.info/models/4x-Nomos2-realplksr-dysample)
10. OpenModelDB. *4x SPANkendata.* [openmodeldb.info/models/4x-SPANkendata](https://openmodeldb.info/models/4x-SPANkendata)
11. OpenModelDB. *4x ClearRealityV1.* [openmodeldb.info/models/4x-ClearRealityV1](https://openmodeldb.info/models/4x-ClearRealityV1)
12. Xintao Wang et al. *Real-ESRGAN Anime Images guide.* [mintlify.com/xinntao/Real-ESRGAN/guides/anime-images](https://www.mintlify.com/xinntao/Real-ESRGAN/guides/anime-images)
13. Fanghua Yu et al. *SUPIR: Scaling Up to Excellence.* arXiv:2401.13627, CVPR 2024. [arxiv.org/html/2401.13627v2](https://arxiv.org/html/2401.13627v2) — cited as the example of a model optimized *against* logo use.
14. Vision Cortex. *VTracer Docs.* [visioncortex.org/vtracer-docs](https://www.visioncortex.org/vtracer-docs)
15. visioncortex. *vtracer README.* [github.com/visioncortex/vtracer](https://github.com/visioncortex/vtracer)
16. Peter Selinger. *Potrace.* [potrace.sourceforge.net](http://potrace.sourceforge.net/)
17. AISVG. *Potrace vs ImageTrace vs VTracer: Complete Comparison Guide.* [aisvg.app/blog/image-to-svg-converter-guide](https://www.aisvg.app/blog/image-to-svg-converter-guide)
18. H3sync. *How to Vectorise AI Generated Logo.* [h3sync.com/blog/how-to-vectorise-ai-generated-logo](https://h3sync.com/blog/how-to-vectorise-ai-generated-logo/)
19. LLMKube Blog. *VecSmith: I Built a Text-to-SVG Pipeline Over a Weekend.* [llmkube.com/blog/vecsmith-weekend-text-to-svg](https://llmkube.com/blog/vecsmith-weekend-text-to-svg)
20. Jan Hendrik Metzen. *Building SVGStud.io: Fine-tuning SDXL for SVG generation.* [jmetzen.github.io/blog/2024/svgstudio-lora](https://jmetzen.github.io/blog/2024/svgstudio-lora/)
21. Recraft. *Recraft V4 SVG on Replicate.* [replicate.com/recraft-ai/recraft-v4-svg](https://replicate.com/recraft-ai/recraft-v4-svg)
22. Recraft. *Ideogram V3 Quality in Recraft.* [recraft.ai/ai-models/ideogram-v3-quality](https://www.recraft.ai/ai-models/ideogram-v3-quality)
23. Recraft. *How to Generate a Logo Using AI.* [recraft.ai/blog/how-to-generate-a-logo-using-ai](https://www.recraft.ai/blog/how-to-generate-a-logo-using-ai)
24. BRIA AI. *RMBG-2.0 model card.* [huggingface.co/briaai/RMBG-2.0](https://huggingface.co/briaai/RMBG-2.0)
25. AIbase. *BiRefNet_HR-matting model overview.* [model.aibase.com/models/details/1915687284843954177](https://model.aibase.com/models/details/1915687284843954177)
26. Remove-Anything. *BiRefNet Algorithm Revolution.* [remove-anything.com/blog/birefnet-ai-background-removal-revolution](https://www.remove-anything.com/blog/birefnet-ai-background-removal-revolution)
27. TheLinuxCode. *OpenCV Sobel Edge Detection (2026 Practical Guide).* [thelinuxcode.com/python-program-to-detect-image-edges-with-opencv-sobel-edge-detection-2026-practical-guide](https://thelinuxcode.com/python-program-to-detect-image-edges-with-opencv-sobel-edge-detection-2026-practical-guide/)
28. Stack Overflow. *Remove background of image using Sobel edge detection.* [stackoverflow.com/questions/78179966](https://stackoverflow.com/questions/78179966/remove-background-of-image-using-sobel-edge-detection)
29. scikit-learn. *Color Quantization using K-Means.* [scikit-learn.org/0.24/auto_examples/cluster/plot_color_quantization.html](https://scikit-learn.org/0.24/auto_examples/cluster/plot_color_quantization.html)
30. ImageMagick. *Reduce the Number of Unique Colors (Quantize API).* [imagemagick.org/api/quantize.php](https://imagemagick.org/api/quantize.php)
31. Wand. *Quantize guide.* [docs.wand-py.org/en/0.6.10/guide/quantize.html](https://docs.wand-py.org/en/0.6.10/guide/quantize.html)
32. Sprite-AI. *Upscale pixel art online — crisp nearest-neighbor scaling.* [sprite-ai.art/tools/upscale-pixel-art](https://www.sprite-ai.art/tools/upscale-pixel-art)
33. Image-Scaler. *Nearest-Neighbor Interpolation: When Pixel-Perfect Matters.* [image-scaler.com/blog/nearest-neighbor-interpolation](https://image-scaler.com/blog/nearest-neighbor-interpolation/)
34. Every Algorithm. *hqx: An Image Scaling Algorithm for Pixel Art.* [every-algorithm.github.io/2024/10/30/hqx.html](https://every-algorithm.github.io/2024/10/30/hqx.html)
35. ssitu. *ComfyUI_UltimateSDUpscale.* [github.com/ssitu/ComfyUI_UltimateSDUpscale](https://github.com/ssitu/ComfyUI_UltimateSDUpscale)
36. ComfyUI Dev. *Ultimate SD Upscale node guide.* [comfyui.dev/docs/guides/nodes/ultimate-sd-upscale](https://comfyui.dev/docs/guides/nodes/ultimate-sd-upscale/)
37. OpenArt. *Txt2Img to SVG Potracer Vector Conversion ComfyUI Workflow.* [openart.ai/workflows/awkward_monster00/txt2img-to-svg-potracer-vector-conversion-example-workflow/bnoXqmR1qQFtBAOCYqod](https://openart.ai/workflows/awkward_monster00/txt2img-to-svg-potracer-vector-conversion-example-workflow/bnoXqmR1qQFtBAOCYqod)
